Practical ai in banking – sas users escoliosis lumbar de convexidad izquierda

Everyone’s excited about artificial intelligence. But most people, in most jobs, struggle to see the how AI can be used in the day-to-day work they do. This post hernia discal lumbar sintomas y signos, and others to come, are all about practical AI. We’ll dial the coolness factor down a notch, but we explore some real gains to be made with dieta blanda para radiografia de columna lumbosacra AI technology in solving business problems in different industries.

I spend a lot of time talking with bankers about AI. It’s fun, but the conversation inevitably turns to concerns around leveraging AI models, which can have some transparency issues, in a highly-regulated and highly-scrutinized industry. It’s a valid concern. However, there are a lot of ways the technology can be used to help banks –even in regulated areas like risk –without disrupting production models and processes.


Banks often need to compute the value of their portfolios. This hernia lumbar tratamiento casero could be a trading portfolio or a loan portfolio. They compute the value of the portfolio based on the current market conditions, but also under stressed conditions or under a range of simulated market conditions. These valuations give an indication of the portfolio’s risk and can inform investment decisions. Bankers need to do these valuations quickly on-demand or in real-time so that they have this information at the time they need to make lumbar hernia symptoms decisions.

However, this isn’t always a fast process. Banks have a lot of instruments (trades, loans) in their portfolios and estenosis lumbar tratamiento the functions used to revalue the instruments under the various market conditions can be complex. To address this, many banks will approximate the true value with a simpler function that runs very quickly. This is often done with first- or second-order taylor series approximation (also called quadratic approximation or delta-gamma approximation) or via interpolation in a matrix of pre-computed values dolor lumbar derecho causas. Approximation is a great idea, but first- and second-order approximations can be terrible substitutes of the true function, especially in stress conditions. Interpolation can suffer the same draw-back in stress.

Machine learning is technology commonly used in AI. Machine learning is what enables computers to find relationships and patterns among data. Technically, traditional first radiografia columna lumbar- order and second-order approximation is a form of classical machine learning, such as linear regression. But in this post we’ll leverage more modern machine learning, like neural networks, to get a better fit with ease.

Neural networks require a lot of data to train the models well. The good thing is we have a lot of data in this case, and we can generate any data we need. We’ll train the escoliosis cervical sintomas network with values of the instruments for many different combinations of the market factors. For example, if we just look at the american hernia discal lumbar tratamiento fisioterapia put option, we’ll need values of that put option for various levels of moneyness, volatility, interest rate, and time to maturity.

Now, start small so you don’t waste time generating tons of data up front. Use relatively sparse data points on each of the market factors escoliosis lumbar levoconvexa but be sure to cover the full range of values so that the model holds up under stress testing. If the model was only trained with interest rates of 3 -5 percent, it’s not going to do well if you stress interest rates to 10 percent. Value the instruments under each combination of values.